The goal of this project is to develop improved statistical methods for human fertility studies. Work has progressed in the development of statistical models that (1) distinguish between different factors underlying fertility, (2) account for mismeasured ovulation day, and (3) characterize heterogeneity among menstrual cycles and women with respect to surrogates of fertility. In the first area, we developed new Bayesian fertility models for distinguishing between factors associated with sterility, multiple ovulation, reduced cycle viability, and embryo loss. These models can also be used to predict biologically meaningful parameters, such as the frequencies of dizygotic twin implantation and birth. In addition, we developed an approach for distinguishing exposure effects on the average level of fertility from effects on the duration of the fertile window. In the second area, we developed an approach for adjusting for bias in the fertility parameters caused by measurement errors in identifying the day of ovulation. In the third area, we develop a Bayesian model that incorporates prior information in estimating patterns of cervical mucus secretions in the menstrual cycle.